• Do COVID case numbers affect Zoom lecture attendance?
• Does weather affect Zoom lecture attendance?

• Weather
  • type (☀️, ☁️, ☔, ❄️, etc.)
  • temperature (T-2, T-1, T-0)
• COVID ( Univ. of Utah, Salt Lake County)
  • Daily case numbers
  • 7-day average

• Scraper
  • Docker-ized
  • Built with Node.js
  • Reports to Discord, ✨automatically✨
  • Pulls weather from OpenWeatherMap
  • Pulls Pulled from COVID Central

• Derek’s plots here

• More of Derek’s plots

• More of Derek’s plots? etc.

• Some of Chris’s analysis

A different approach. 🤠 Data wrangling:

data <- read.csv("data/atd_weather_full.csv") %>%
    rowwise() %>%
    mutate(mean_atd = mean(c(atd_start, atd_mid, atd_end))) %>%
    mutate(atd_rate = mean_atd / enrolled) %>%
    mutate(course = factor(course)) %>%
    mutate(weather = factor(weather)) %>%
    mutate(date = as.Date(date, "%m/%d/%Y")) %>%
    mutate(mean_temp = mean(temp_tm2, temp_tm1, temp_tm0, na.rm = TRUE))

First, Zoom attendance rate versus mean temp:

Zoom attendance rate versus the 7-day average of new COVID cases at the :

So, does this mean me might see relationships within individual classes?

…

Let’s find out!

3200, 3500, and 4400 look promising: we’ll make a plot matrix:

p3500 <- data_filtered %>%
    filter(course == 3500) %>%
    select(!course) %>%
    ggpairs() +
    theme(axis.text.x = element_text(angle = 90, hjust = 1),
          axis.title = element_text(size = rel(0.85))) +
    labs(title = "3500 Pairs")

(repeat as needed)

• Zoom Attendance Rate vs. Mean Temp
  • CS 3500: (\(p > 0.05\)) – Not significant.
  • CS 3200: \(-0.564,\ r^2 = 0.318\) (\(p < 0.01\))
  • CS 4400: \(0.6,\ r^2 = 0.36\) (\(p < 0.01\))

• Zoom Attendance Rate vs. 7-day Avg Cases at
  • CS 3500: \(-0.124,\ r^2 = 0.015\) (\(p < 0.001\))!!
  • CS 3200: \(-0.564,\ r^2 = 0.318\) (\(p < 0.01\))
  • CS 4400: (\(p > 0.05\)) – Not significant.

• Limitations too great to make assertions…